color language Christina M. Frederickkay//LensAgingAndGrue.pdf · 2004. 11. 30. · Color naming for stimuli that were nominally green, blue-green or blue was ... Younger observers

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Color naming, lens aging, and grue: What the optics of the aging eye can teach us aboutcolor language

Joseph L. Hardy*

Department of Ophthalmology and Section of Neurobiology, Physiology and Behavior

University of California, Davis Medical Center

Christina M. Frederick

Department of Psychology, University of California, Berkeley

Paul Kay

International Computer Science Institute, Berkeley, California

John S. Werner

Department of Ophthalmology and Section of Neurobiology, Physiology and Behavior

University of California, Davis Medical Center

Word count: 3956References: 32

* Corresponding authorDepartment of Ophthalmology4860 Y St., Suite 2400Sacramento, CA 95817Call (916) 734-4541Fax (916) 734-4543E-mail: [email protected]

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Abstract

Many languages without separate terms for “green” and “blue” are or were

spoken in locations receiving above-average exposure to ultraviolet-B (UV-B) radiation.

Lindsey and Brown (2002) propose that this correlation is caused by premature lens

aging. When younger observers view simulated paint chips filtered through the

equivalent of an older observer’s lens – removing much short-wavelength light – they use

the term blue less often than when describing the unfiltered versions. Some stimuli that

were called blue without simulated aging were called green when filtered. However,

when we tested older observers with known ocular media optical densities we found no

difference between older and younger subjects in the proportion of blue color-name

responses. Color naming for stimuli that were nominally green, blue-green or blue was

virtually identical for older and younger observers when viewing the same (unfiltered)

stimuli. Our results are inconsistent with Lindsey and Brown’s (2002) lens brunescence

hypothesis.

Introduction

Most humans can discriminate millions of colors, yet in every language studied

systematically, the number of basic color terms is comparatively small. In English, for

example, there are 11 basic color terms (BCTs), i.e., black, white, red, yellow, green,

blue, purple, brown, orange, pink, and gray (Berlin & Kay, 1969). BCTs refer to the

smallest collection of simple words with which a speaker can name any color. A few

languages have more than 11 BCTs: Russian has distinct BCTs for light blue and dark

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blue, Hungarian for light red and dark red, but many languages have fewer. Welsh and

most other Celtic languages do not have distinct BCTs for green and blue (Lazar-Meyn,

2004), and the same is true for almost all unwritten languages. Color term distribution is

not random. Rather, there is a distinct tendency for languages to evolve color terms in a

particular order. For example, no language is known to have distinct words for blue and

green and yet fail to distinguish red and yellow. The origins and mechanisms underlying

the regularity of color-naming across languages has been the topic of considerable

investigation (e.g., Heider, 1972; Kay & McDaniel, 1978; Kay & Maffi, 1999) and

debate (e.g., Hickerson, 1975; Lucy, 1997; Davidoff, Davies & Roberson, 1999).

Recently, Lindsey and Brown (2002) have offered an interesting hypothesis to

explain why some languages lack BCTs that distinguish green and blue. According to

their argument, most so-called grue languages (those with a single term covering both

green and blue) occur in geographical locations closer to the equator or at higher

elevations and receive above-average levels of ultraviolet-B (UV-B) radiation from the

sun. High UV-B exposure is linked to accelerated ocular media aging, most particularly

of the crystalline lens (Werner, Peterzell & Scheetz 1990; Young, 1991; Javitt & Taylor,

1994). As the crystalline lens ages, a process known as brunescence occurs. The lens

becomes denser and more opaque, allowing less light to reach the retina, especially at

shorter wavelengths (Weale, 1988). Individuals experiencing premature lens aging

would receive less short-wavelength light at the retina when viewing the same stimuli as

people with more transparent lenses. Lindsey and Brown (2002) argue that this reduced

short-wavelength light exposure would reduce the need for the color term “blue,” usually

assigned to stimuli predominantly composed of energy at shorter visible wavelengths.

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Thus, they propose that languages spoken by cultures located in high UV-B areas would

tend to be grue languages. In support of this hypothesis, Lindsey and Brown (2002)

performed a color-naming experiment in which they simulated the effects of lens

brunescence in younger individuals. This simulation took advantage of the lens-aging

model of Pokorny, Smith and Lutze (1987) to transform stimulus chromaticities to

simulate the effects of optical aging. Younger observers were shown simulated Munsell

chips (a set of color standards) made yellower and darker in the exact proportions

prescribed by the Pokorny, et al. (1987) lens model to simulate ages between 50-100

years. As predicted by Lindsey & Brown’s (2002) lens brunescence hypothesis,

observers used the term “blue” progressively less often as the simulated lens was

increasingly aged. Chips previously called “blue” were called “green” or “gray” after

being transformed by simulated aging. Lindsey and Brown (2002) concluded that the

results of this experiment show that premature lens brunescence could lead to a reduced

need for the term blue. This argument has created considerable interest and some

controversy (e.g., Lazar-Meyn, 2004; Lindsey and Brown, 2004; Regier and Kay, 2004).

While Lindsey and Brown’s (2002) aging simulation was mathematically

consistent with a valid model of lens brunescence, the effects of lens aging can be tested

more directly. This can be achieved by testing individuals of various chronological ages

and, thus, a range of ocular media optical densities (ODs). The results of this more direct

test could differ from a simulation. Younger observers viewing stimuli designed to

simulate the optics of the aging eye experience lower light levels and higher proportions

of long-wavelength light for a matter of minutes. Older individuals with brunescent

lenses live with their optics continuously, as do people with more brunescent lenses from

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high UV-B areas. While Lindsey and Brown (2002) did have observers briefly (3

minutes) adapt to the background color previous to the experiment, chromatic adaptation

can operate on very long time scales, on the order of days (Neitz, Carroll, Yamauchi,

Neitz, & Williams, 2002) or even months (Delahunt, Webster, Ma, & Werner, in press).

Chromatic adaptation has the effect of changing how the visual system interprets the light

reaching each of the three cone photoreceptor types (Jameson & Hurvich, 1956), which

can largely compensate for the changes in the average spectral distribution of light

reaching the retina (Uchikawa, Uchikawa, & Boynton, 1989). Thus, while such a

simulation can closely replicate the pattern of wavelengths reaching the observer’s retina

with differing ocular media ODs, it is unlikely to accurately simulate the perceptual

experience of observers with naturally yellowed lenses.

To test Lindsey and Brown’s (2002) lens-brunescence hypothesis we compared

color naming in individuals with a range of known ocular media ODs. We did this in two

ways. In one set of conditions, we tested color naming in groups of younger and older

observers with the same standard stimulus set used by Lindsey and Brown (2002). In a

separate set of conditions, we tested color naming in the same groups, but using stimuli

that simulated the effects of ocular media present in the complementary age group (i.e.,

younger observers viewed stimuli filtered through the simulated ocular media of older

observers and older observers viewed stimuli filtered through the simulated ocular media

of younger observers).

If the Lindsey and Brown (2002) proposal is correct, color naming by older

observers in the standard condition should be similar to color naming by younger

observers in the simulated aging condition. Also, color naming by younger observers in

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the standard condition should be similar to color naming by older observers in the

simulated youthening condition. Specifically, stimuli denoted in the Munsell color

system as “blue-green” and “blue” should be identified with the color name “green” more

often by older subjects viewing standard stimuli and younger subjects viewing simulated

aging stimuli than by younger subjects viewing standard stimuli and older subjects in the

simulated youthening condition. Additionally, in the standard stimulus conditions, there

should be a strong negative correlation between ocular media OD and use of the color

term “blue.”

Evidence from aging studies of unique hue loci (Schefrin & Werner, 1990), hue

scaling (Schefrin & Werner, 1993), and color naming in Japanese (Okajima, Yamashita,

Takamura, Watanabe, & Tsuchiya, 2002), however, cast doubt on Lindsey and Brown’s

predictions. These studies show that older observers tend to use linguistic color

descriptors in much the same way as younger observers. This is despite their increased

ocular media OD and suggests that the color naming of older individuals should be

similar to that of younger observers when presented with the same stimuli.

Methods

Observers

All observers were born in the United States and were native English speakers.

Observers were provided complete ophthalmic and optometric examinations prior to

inclusion in this study. Only visually healthy (e.g., absence of clinically significant

cataracts or retinal disorders) observers were considered for this experiment. No

observers had a history of cataract surgery, and thus, all observers had intact crystalline

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lenses. This study included 20 observers in two age groups. There were 10 observers in

the younger group (mean = 23.2 years of age, range = 18-29) and 10 in the older group

(mean = 73.9 years of age, range = 68-79). The ratio of males to females in both groups

was 1:1. Written informed consent was obtained before testing, based upon a protocol

approved by the Office of Human Research Protection of the University of California,

Davis, School of Medicine.

Ocular Media Measurement

Prior to participating in the color-naming portion of this experiment, we

determined the ocular media OD for one eye of each individual. Ocular media OD was

estimated for each observer based upon their scotopic (low light level) spectral

sensitivity, using a variation of a technique described by Norren and Vos (1974). The

rationale for this method is that under scotopic conditions, relative spectral sensitivity is

dependent upon the shape of the rhodopsin (rod photopigment) absorption spectrum and

the ocular media OD spectrum. The absorption spectrum of rhodopsin is essentially

invariant between individuals. Thus, any differences between observers in relative

sensitivity to various wavelengths of light under scotopic conditions are due to ocular

media OD differences. Differences in sensitivity between individual observers and the

Commission Internationale de l’Eclairage (CIE) standard observer for scotopic spectral

sensitivity (V__) were fitted with varying amounts of ocular media OD in proportions

prescribed by the lens-aging model of Pokorny, et al. (1987). The function that provided

the best least-squares fit to these data was taken as the individual’s ocular media OD (see

Figure 1).

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To measure the scotopic spectral sensitivity function, we used a Maxwellian-view

optical system that permitted monochromatic light (7 wavelengths from 410 – 600 nm) to

be imaged on the retina as an annulus (7-15°, inner and outer diameters) flickering as a 3

Hz square wave at 100% modulation. The observer’s task was to adjust stimulus intensity

by varying a neutral density wedge with a potentiometer until the stimulus was just

detected. Following 30 minutes of dark adaptation, and a minimum of three practice

trials, observers made a series of three threshold settings for each wavelength, presented

in random order. Sensitivity was defined by the reciprocal of the energy at the geometric

mean setting.

Color-Naming Experiment

In the standard stimulus conditions, stimuli were colorimetrically-simulated

versions of the 40 Value-6 Munsell chips used in the 1997 World Color Survey (Kay,

Berlin, Maffi, & Merrifield, 1997) presented on a CRT. The chromaticities used were

from Newhall, Nickerson, and Judd (1943) who made colorimetric measurements of

these chips under CIE Illuminant C. In the simulated aging and simulated youthening

conditions, these stimuli were adjusted to simulate the effects of the ocular media OD of

an average observer in the complementary age group. In the simulated aging conditions,

the younger individual’s ocular media OD was subtracted from the OD for an average 75-

year-old observer (values taken from the Pokorny, et al., 1987, model for that age). This

difference in OD was then subtracted, wavelength-by-wavelength, from the log10 of the

energy spectra emitted by the CRT phosphors for the simulated chip, which was

measured with a spectroradiometer (Spectra-Prichard PR703A) for each stimulus from

the standard condition. The resulting function represents the light that would be

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necessary to present to younger observers on the CRT to produce the same retinal

stimulation that the older observer would receive from the standard stimulus. The

chromaticity of this spectral distribution was calculated, and the proportions of the three

phosphor types necessary to produce this chromaticity were computed. This stimulus set

will be referred to as the simulated aging condition. These stimuli are analogous to the

simulation performed by Lindsey and Brown (2002), with the exception that we

measured the OD of each individual’s ocular media while Lindsey and Brown (2002)

used standard values. The simulated youthening stimuli were prepared in a

complementary fashion. Standard stimuli were adjusted for older observers, based on

their measured ocular media OD, to recreate the retinal stimulation of an observer with

the ocular media OD of the average 25 year old from the Pokorny, et al. (1987) model.

The color-naming stimuli were presented in circular (4.4o visual angle) test

patches surrounded by a uniform gray field (28.7 o x 21.5 o) on a Sony G-200 CRT. The

chromaticity of the surround was (0.310, 0.316) in CIE 1931 chromaticity coordinates,

equivalent to CIE illuminant C. The luminance of the background was 10 cd/m2 for the

standard stimuli. Test patch luminance was set to twice the surround for the standard

stimulus conditions. In the simulated aging and youthening conditions, the actual

luminance values of the test patch and surround depended on the measured values of

ocular media OD. Stimuli were presented in a darkened laboratory at a viewing distance

of 63.5 cm. All observers were properly refracted for the test distance using trial lenses.

Stimuli were presented monocularly to the eye that was measured for ocular media OD.

Each observer participated in two conditions: the standard condition and a simulated

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aging or youthening condition. In each condition, 40 stimuli were presented four times

over the course of two sessions. Each observer began with one practice session.

Stimulus presentation was preceded by 10 minutes of dark adaptation. This was

followed by 3 minutes of light adaptation to the mean background color for the condition

to be tested. On each trial the simulated Munsell chip was presented for 1 sec.

Following stimulus presentation, observers chose a single color name using a computer

mouse, from a provided list of the 11 English BCTs (red, orange, yellow, green, blue,

purple, white, black, brown, grey, and pink).

RESULTS

Figure 2 displays the color names chosen most often for each of the 40 simulated

Munsell chips for younger and older observers in the standard and simulated aging and

youthening conditions. The results of the simulated aging condition replicate Lindsey

and Brown’s (2002) major experimental findings. Observers in the simulated aging

condition used the term “green” for two chips in the blue-green category of the Munsell

designation that were named “blue” most often in the standard condition. In addition,

chips consistently named “purple” in the standard conditions were named “pink” more

frequently in the simulated aging condition. The simulated youthening condition is also

consistent with the results and analysis of Lindsey and Brown (2002). There was a shift

under these conditions toward using “blue” more often when naming chips in the blue-

green Munsell designation that were named “green” most often in the standard condition.

Older observers called one chip “yellow” in this condition that was called “orange” in the

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standard condition. There was also a tendency for these observers to use “purple” for

some chips in the simulation that were named “pink” in the standard.

While the results from our aging and youthening simulations replicated Lindsey

and Brown’s (2002) major findings, the comparison of the results from the older and

younger observers in the standard condition contradicted their lens-brunescence

hypothesis. According to this hypothesis, the older observers’ color-naming responses

under standard conditions should be more similar to the younger observers’ responses

from simulated aging than from the standard condition. Most importantly, in the standard

condition, older observers should use “green” for some stimuli designated “blue” by

younger observers, just as is seen in simulated aging. However, when older and younger

observers were presented physically-identical stimuli, color-naming responses were very

similar. Figure 3 shows the proportion of “green” and “blue” responses for the green-

yellow, green, blue-green, blue and purple-blue Munsell colors. The upper panel

compares older (solid lines) and younger (dashed lines) observers from the standard

stimulus condition. Clearly, there is little difference between younger and older

observer’s responses for these stimuli, especially in the blue-green region. For the

stimuli represented in Figure 3, the response proportions for older and younger observers

in the standard condition are highly correlated for both “green” (R2 = 0.95) and “blue”

(R2 = 0.99). The lower panel compares the results of the older observers from the

standard condition (solid lines – same data as above) and young observers from the

simulated aging condition (dashed lines). Younger subjects in the simulated aging

condition used “green” most often for several stimuli that older subjects called “blue.”

Correlations for color-name proportions for simulated aging and older observers in the

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standard condition were lower than what was observed when both groups received the

same physical stimuli (R2 for “green” = 0.80 and R2 for “blue” = 0.81).

The lens brunescence hypothesis predicts a strong negative relation between use

of the term “blue” and ocular media OD for the stimuli tested in this experiment. This

prediction is evident in a comparison of younger subjects in the standard and simulated

aging conditions. Younger subjects used the term “blue” significantly more often as a

proportion of total responses in the standard condition than in the simulated aging

condition (p<0.05, two-tailed t-test). There was no significant difference between the

proportion of total responses for “blue” for older and younger subjects in the standard

conditions (p=0.49). Figure 4 shows the proportion of all trials in the standard condition

in which the observer selected the color term “blue” to describe the stimulus as a function

of ocular media OD of each observer at 400 nm. The regression is not significant. The

slope of the regression line is -0.001 (R2 = 0.009, p = 0.89). We cannot reject the null

hypothesis that there is no relation between ocular media OD and use of the color term

“blue” in normal aging.

DISCUSSION

Lindsey and Brown (2002) highlight the interesting correlation between high-UV-

B exposure and the absence of a verbal distinction between the color terms “blue” and

“green.” This is particularly interesting as high-UV-B exposure is also correlated with

accelerated lens brunescence (Werner, Peterzell & Scheetz 1990; Young, 1991; Javitt &

Taylor, 1994). Based on these relations, they suggest a potential physiological

mechanism linking these two correlations. They hypothesize that, because short

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wavelength light is absorbed by the crystalline lens in greater proportion with increasing

UV-B exposure, people exposed to more UV-B light do not require the verbal distinction

between “blue” and “green.” This model offers a potentially elegant explanation for the

geographic distribution of blue-green relative to grue languages based entirely on a

simple physiological process.

Our replication of Lindsey and Brown’s lens brunescence simulation confirmed

their results. When younger observers were presented filtered stimuli simulating the

spectral composition of light that would reach an older observer’s eye for the same

stimulus, they used the color term “blue” less often and the color term “green” in its

place. However, when the same physical stimuli were presented to younger and older

observers, the two groups named the relevant stimuli in virtually the same way. There

was no significant relation between ocular media OD and use of the color terms “blue”

and “green.” This result fails to support the lens-brunescence hypothesis. We infer from

our results that premature lens brunescence due to increased UV-B exposure could not

serve as a causal mechanism to explain the lack of a linguistic distinction between blue

and green.

The older observers in our study had ocular media absorption values that were, on

average, ~1 log unit greater at 400 nm than those of the younger observers. The

measured difference between the most and least dense ocular media in our study was 1.6

log units. The ocular media of the young observer with the least opaque lens and cornea

transmitted 41 times more light at 400 nm than the ocular media of the older observer

with the most opaque lens and cornea. Despite these large differences in the filtering of

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short wavelength light, younger and older observers used the terms “blue” and “green” in

much the same way for physically-identical stimuli.

People differ widely in ocular media OD, both across the lifespan and among

individuals of similar age. Despite these large individual differences, people within the

same culture describe physically identical stimuli similarly on many measures of color

appearance, including unique hue loci (Schefrin & Werner, 1990) and hue scaling

(Schefrin & Werner, 1993). Additionally, differences in light-source spectral

composition across viewing situations (e.g., the sun vs. a fluorescent light bulb) are such

that the light reaching the retina from any given object will differ greatly from situation

to situation. Despite the vastly different spectral composition of light reaching our retina

across our life span and across different viewing conditions, we generally refer to a given

object with a particular color term. If this were not the case, color names would not be

useful linguistic tools.

For this uniformity to be possible, the visual system must compensate for changes

in the spectral composition of light reaching the retina. This compensatory process is

referred to as color constancy and has been the subject of systematic theoretical and

experimental investigation for well over 100 years (e.g., Helmholtz, 1911; Hering, 1920).

While the mechanisms underlying this process are not fully understood, chromatic

adaptation and surround effects likely play critical roles (Kraft & Brainard, 1999). An

individual’s entire visual world is filtered through their ocular media. The visual system

is likely able to discount the ocular media spectral filtering effects due to this consistency

across space and over time.

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Lindsey and Brown reference an apparently competing sociolinguistic explanation

for the absence of a blue/green lexical distinction in languages of these groups living in

high UV-B regions (Berlin & Kay, 1969; Kay & Maffi, 1999). This argument is

supported by the observation that distance from the equator, level of technology, and

number of basic color terms are all positively correlated (Naroll, 1970; Hays, Margolis,

Naroll, & Perkins, 1972; Ember, 1978); it holds that a driving force in increasing the

number of color terms over time could be, as Lindsey and Brown acknowledge, that,

“...as a culture becomes technologically more complex, speakers have more frequent

need to distinguish objects by their colors” (p. 512). The reason tropical, and hence high

UV-B, societies tend to be less technologically advanced and vice versa, although

unexplained, is beside the point currently at issue. Despite Lindsey and Brown’s

welcome attempt to offer a physiological mechanism for the lack of a lexical blue/green

distinction, other aspects of observed cross-language color naming pose a problem for

this claim beyond the experimental data presented here. The most notable characteristic

of the color term systems of the languages of low technology groups is not so much the

lexical merger of green and blue per se, as it is the merger of several specific pairs and

triples of basic colors into single terms (Kay & Maffi, 1999). For example, while the

inclusion of red and yellow in a single term is less frequent than the inclusion of green

and blue, languages that contain this merger are similarly distributed across the globe (see

Lindsey and Brown, 2002, Figure 2). Such an inclusion could not be explained by

increased lens brunescence, since the wavelengths critical for this distinction pass

through the ocular media virtually unfiltered. A model of color-name evolution must

account not only for the geographical distribution of languages that lack distinct terms for

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blue and green, but also for the distribution of languages that lack other lexical

distinctions, notably the red/yellow distinction. The correlation between UV-B radiation

and the absence of terms meaning blue is likely not caused by accelerated brunescence of

the ocular lens, but is likely related to some additional third factor that is correlated with

both phenomena.

Acknowledgements: This work was supported by the National Institute on Aging (grant

AG04058) and a Jules and Doris Stein Research to Prevent Blindness Professorship.

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FIGURE LEGENDS

Figure 1. Scotopic sensitivity functions for 27 year-old (upper left panel) and 73 year-old

(upper right panel) observers. Solid curves are the CIE’s standard observer scotopic

sensitivity function (V__), while the dashed curves are scotopic sensitivity functions

adjusted for ocular media OD based on the Pokorny, et al. (1987) model. The lower

panels show the estimated ocular media OD functions for the two subjects based on the

scotopic sensitivity functions above.

Figure 2. Modal color names for the 40 Value-6 Munsell hues used in the World Color

Survey. Box color corresponds to the color name given to that stimulus most often. The

dark colored boxes reflect ≥ 80% agreement amongst observer responses. The light

colored boxes reflect < 80% agreement amongst observer responses. Letters across the

top correspond to the nominal colors of the simulated Munsell chips. Rows A and B

illustrate modal responses in the standard stimulus condition, for older and younger

groups, respectively. Row C shows observer modal responses from the older group for

the simulated youthening condition. Row D shows observer modal responses from the

younger group for the simulated aging condition.

Figure 3. Proportion responses for “green” and “blue” as a function of Munsell hue

stimulus. Gray and black curves and symbols denote “green” and “blue” responses,

respectively. The upper panel plots data from the standard stimulus conditions for older

(filled symbols and solid lines) and younger (open symbols and dashed lines) observers.

The lower panel plots data from older observers in the standard stimulus condition (filled

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symbols and solid lines; same as upper panel) and younger subjects in the simulated

aging condition (open symbols and dashed lines). Letters across the bottom correspond

to the nominal colors of the simulated Munsell chips.

Figure 4. The proportion of the trials on which an individual used the color name “blue”

a function of ocular media OD at 400 nm.

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REFERENCES

Berlin, B., & Kay, P. (1969). Basic color terms: Their universality and evolution.

Berkeley, CA: University of California Press.

Davidoff, J., Davies, I., & Roberson, D. (1999). Colour categories in a stone-age tribe.

Nature, 398, 203-204.

Delahunt, P. B., Webster, M. A., Ma, M., & Werner, J. S. (in press). Long-term

renormalization of chromatic mechanisms following cataract surgery. Visual

Neuroscience.

Ember, M. (1978). Size of color lexicon: Interaction of cultural and biological factors.

American Anthropologist, 80, 364-367.

Hays, D. G., Margolis, E., Naroll, R., & Perkins, D. R. (1972). Color term salience.

American Anthropologist, 74, 1107-1121.

Heider, E. R. (1972). Universals in color naming and memory. Journal of Experimental

Psychology, 93, 10-20.

20

Helmholtz, H. von. (1911). Handbuch der Physiologischen Optik. Translated as

Helmholtz’s Treatise on physiological optics (J. P. C. Southall, Trans. 3rd ed.).

New York: Dover Publications (translation 1962).

Hering, E. (1920). Zur Lehre vom Lichtsinne. Translated as Outlines of a theory of the

light sense (L. M. Hurvich & D. J. Jameson, Trans.). Cambridge, MA: Harvard

University Press (translation 1964).

Hickerson, N. P. (1971). [Review of the book Basic color terms: Their universality and

evolution]. International Journal of American Linguistics, 37, 257-270.

Jameson, D., & Hurvich, L. M. (1956). Some quantitative aspects of an opponent-colors

theory. III. Changes in brightness, saturation, and hue with chromatic adaptation.

Journal of the Optical Society of America, 46, 405-415.

Javitt, J. C., & Taylor, H. R. (1994-1995). Cataract and latitude. Documenta

Ophthalmologica, 88, 307-325.

Kay, P., Berlin, B., Maffi, L., & Merrifield, W. (1997). Color naming across languages.

In C. L. Hardin and L. Maffi (Eds.), Color categories in thought and language

(pp. 21-56). New York: Cambridge University Press.

21

Kay, P., & Maffi, L. (1999). Color appearance and the emergence and evolution of basic

color lexicons. American Anthropologist, 101, 743-760.

Kay, P., & McDaniel, C. K. (1978). The linguistic significance of the meanings of basic

color terms. Language, 54, 610-646.

Kraft, J. M., & Brainard, D. H. (1999). Mechanisms of color constancy under nearly

natural viewing. Proceedings of the National Academy of Sciences of the United

States of America, 96, 307-312.

Lazar-Meyn, H. A. (2004). Color naming: "grue" in the Celtic languages of British Isles.

Psychological Science, 15, 288.

Lindsey, D. T., & Brown, A. M. (2002). Color naming and the phototoxic effects of

sunlight on the eye. Psychological Science, 13, 506-512.

Lindsey, D. T., & Brown, A. M. (2004). Sunlight and "Blue": the prevalence of poor

lexical color discrimination within the "grue" range. Psychological Science, 15,

291-294.

Lucy, J. A. (1997). The linguistics of color. In C. L. Hardin and L. Maffi (Eds.), Color

categories in thought and language (pp. 320-346). New York: Cambridge

University Press.

22

Naroll, R. (1970). What have we learned from cross-cultural surveys? American

Anthropologist, 72, 1227-1288.

Neitz, J., Carroll, J., Yamauchi, Y., Neitz, M., & Williams, D. R. (2002). Color

perception is mediated by a plastic neural mechanism that is adjustable in adults.

Neuron, 35, 783-792.

Newhall, S. M., Nickerson, D., & Judd, D. B. (1943). Final report of the O.S.A.

subcommittee on spacing of the Munsell colors. Journal of the Optical Society of

America, 33, 385-418.

Norren, D. V., & Vos, J. J. (1974). Spectral transmission of the human ocular media.

Vision Research, 14, 1237-1244.

Okajima, K., Yamashita, K., Takamura, Y., Watanabe, K., & Tsuchiya, N. (2002,

November/December). Color perception of the elderly: Experiments and

simulations. Paper presented at the meeting of the International Conference for

Universal Design, Yokohama, Japan.

Pokorny, J., Smith, V. C., & Lutze, M. (1987). Aging of the human lens. Applied

Optics, 26, 1437-1440.

23

Regier, T., & Kay, P. (2004). Color naming and sunlight: commentary on Lindsey and

Brown (2002). Psychological Science, 15, 289-290.

Schefrin, B. E., & Werner, J. S. (1990). Loci of spectral unique hues throughout the life

span. Journal of the Optical Society of America A, 7, 305-311.

Schefrin, B. E., & Werner, J. S. (1993). Age-related changes in the color appearance of

broadband surfaces. Color Research and Application, 18, 380-389.

Uchikawa, K., Uchikawa, H., & Boynton, R. M. (1989). Partial color constancy of

isolated surface colors examined by a color-naming method. Perception, 18, 83-

91.

Weale, R. A. (1988). Age and the transmittance of the human crystalline lens. Journal of

Physiology, 395, 577-587.

Werner, J. S., Peterzell, D. H., & Scheetz, A. J. (1990). Light, vision, and aging.

Optometry and Vision Science, 67, 214-229.

Young, R. W. (1991). Age related cataract. New York: Oxford University Press.

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Figure 2.

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Figure 3.

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Figure 4.